title: Evidence synthesis for decision making 6: embedding evidence synthesis in probabilistic cost-effectiveness analysis.
abstract: When multiple parameters are estimated from the same synthesis model, it is likely that correlations will be induced between them. Network meta-analysis (mixed treatment comparisons) is one example where such correlations occur, along with meta-regression and syntheses involving multiple related outcomes. These correlations may affect the uncertainty in incremental net benefit when treatment options are compared in a probabilistic decision model, and it is therefore essential that methods are adopted that propagate the joint parameter uncertainty, including correlation structure, through the cost-effectiveness model. This tutorial paper sets out 4 generic approaches to evidence synthesis that are compatible with probabilistic cost-effectiveness analysis. The first is evidence synthesis by Bayesian posterior estimation and posterior sampling where other parameters of the cost-effectiveness model can be incorporated into the same software platform. Bayesian Markov chain Monte Carlo simulation methods with WinBUGS software are the most popular choice for this option. A second possibility is to conduct evidence synthesis by Bayesian posterior estimation and then export the posterior samples to another package where other parameters are generated and the cost-effectiveness model is evaluated. Frequentist methods of parameter estimation followed by forward Monte Carlo simulation from the maximum likelihood estimates and their variance-covariance matrix represent'a third approach. A fourth option is bootstrap resampling--a frequentist simulation approach to parameter uncertainty. This tutorial paper also provides guidance on how to identify situations in which no correlations exist and therefore simpler approaches can be adopted. Software suitable for transferring data between different packages, and software that provides a user-friendly interface for integrated software platforms, offering investigators a flexible way of examining alternative scenarios, are reviewed.
authors.affiliation: School of Social and Community Medicine, University of Bristol, Bristol, UK (SD, NJW, AEA)
authors.forename: Sofia
authors.initials: S
authors.lastname: Dias
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, UK (AJS)
authors.forename: Alex J
authors.initials: AJ
authors.lastname: Sutton
authors.affiliation: School of Social and Community Medicine, University of Bristol, Bristol, UK (SD, NJW, AEA)
authors.forename: Nicky J
authors.initials: NJ
authors.lastname: Welton
authors.affiliation: School of Social and Community Medicine, University of Bristol, Bristol, UK (SD, NJW, AEA)
authors.forename: A E
authors.initials: AE
authors.lastname: Ades
mesh_terms: D003362:Cost-Benefit Analysis; D003657:Decision Making; D019317:Evidence-Based Medicine; D009010:Monte Carlo Method; D011336:Probability; D012984:Software
keywords: cost-effectiveness analysis; evidence synthesis; network meta-analysis; probabilistic sensitivity analysis